arXiv:2606.07066v1 Announce Type: new Abstract: Semantic association between a word and its context has been identified as an important component of reading comprehension, even when word predictability is accounted for. Recent research has highlighted the potential of language model ( LM) embeddings to quantify semantic association. Yet, embedding-based semantic association have been operationalized in a myriad of ways. In this study, we use embeddings from LMs to estimate semantic association on a corpus of joint electroencephalography (EEG) and self-paced reading of natural, Dutch texts. Sem
The proliferation of advanced language models provides new tools for granular analysis of cognitive processes, pushing the boundaries of psycholinguistics research.
This research provides a deeper, quantifiable understanding of human reading comprehension, which is critical for developing more sophisticated and human-aligned AI language models and educational tools.
The operationalization of LM embeddings for semantic association offers a more precise method for studying cognitive load and comprehension during reading, moving beyond traditional psycholinguistic metrics.
- · AI/ML researchers
- · Cognitive science
- · EdTech developers
- · NLP developers
- · Traditional psycholinguistic methods
- · Less data-driven cognitive models
Improved understanding of human language processing contributes to more effective human-AI interaction designs.
Development of adaptive learning systems that tailor content based on real-time cognitive load detection using such models.
Potential for AI systems to not just understand but also 'feel' or 'comprehend' text in a more human-like manner, leading to advanced AI agents.
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